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View Code? Open in Web Editor NEWTHOI: An efficient library for higher order interactions analysis based on Gaussian copulas enhanced by batch-processing
License: MIT License
THOI: An efficient library for higher order interactions analysis based on Gaussian copulas enhanced by batch-processing
License: MIT License
This function would be a wrapper of nplet_measures to allow user to compute very large list of nplets in multiple batched using a pytorch dataloader
In both simulated annealing and greedy we use the o information as optimization score. But we could want to use tc, dtc or s and we can set this as parameter
The idea is to be able to be consisten and work all in torch if required. The function was let in numpy because is only computed once at the begining but it could be the case we want to perform this operation repeated times eficiently in GPU
Stop calculating the determinants in _get_tc_dtc_from_batched_covmat and directly use torch.logdet in _gaussian_entropy_estimation
In some cases, the user could have already computed the covariance matrix and not the time series, then the funtion does not need to estimate the covariance using gaussian copula.
This case can happen when working with sintetic data or theoretical systems.
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